Introducción
Los tipos de datos de Python se utilizan para definir el tipo de una variable. En este artículo, enumeraremos todos los tipos de datos y analizaremos la funcionalidad de cada uno.
Diferentes tipos de tipos de datos de Python
Existen muchos tipos de datos diferentes en Python. Algunos de los tipos de datos integrados en Python son:
- Tipos de datos numéricos: int, float, complex
- Tipos de datos de cadena: str
- Tipos de secuencia: lista, tupla, rango
- Tipos binarios: Bytes, Bytearray, memoryview
- Tipo de datos de mapeo: dict
- Tipo booleano: bool
- Tipos de datos del conjunto: Conjunto, Conjunto congelado
Tipo de datos numéricos de Python
Python numeric data type is used to hold numeric values like;
1. int - holds signed integers of non-limited length.
2. long- holds long integers(exists in Python 2.x, deprecated in Python 3.x).
3. float- holds floating precision numbers and it's accurate up to 15 decimal places.
4. complex- holds complex numbers.
In Python, we need not declare a datatype while declaring a variable like C or C++. We can simply just assign values in a variable. But if we want to see what type of numerical value is it holding right now, we can use **type()**, like this:
```
#create a variable with integer value.
a=100
print("The type of variable having value", a, " is ", type(a))
#create a variable with float value.
b=10.2345
print("The type of variable having value", b, " is ", type(b))
#create a variable with complex value.
c=100+3j
print("The type of variable having value", c, " is ", type(c))
```
If you run the above code you will see output like the below image. Tipo de datos de cadena de Python
The string is a sequence of characters. Python supports Unicode characters. Generally, strings are represented by either single or double-quotes.
```
a = "string in a double quote"
b= 'string in a single quote'
print(a)
print(b)
# using ',' to concatenate the two or several strings
print(a,"concatenated with",b)
#using '+' to concate the two or several strings
print(a+" concated with "+b)
```
The above code produces output like the below picture- Tipo de datos de lista de Python
The list is a versatile data type exclusive in Python. In a sense, it is the same as the array in C/C++. But the interesting thing about the list in Python is it can simultaneously hold different types of data. Formally list is an ordered sequence of some data written using square brackets(\[\]) and commas(,).
```
#list of having only integers
a= [1,2,3,4,5,6]
print(a)
#list of having only strings
b=["hello","john","reese"]
print(b)
#list of having both integers and strings
c= ["hey","you",1,2,3,"go"]
print(c)
#index are 0 based. this will print a single character
print(c[1]) #this will print "you" in list c
```
The above code will produce output like this- Tupla de Python
The tuple is another data type which is a sequence of data similar to a list. But it is immutable. That means data in a tuple is write-protected. Data in a tuple is written using parenthesis and commas.
```
#tuple having only integer type of data.
a=(1,2,3,4)
print(a) #prints the whole tuple
#tuple having multiple type of data.
b=("hello", 1,2,3,"go")
print(b) #prints the whole tuple
#index of tuples are also 0 based.
print(b[4]) #this prints a single element in a tuple, in this case "go"
```
The output of this above python data type tuple example code will be like the below image. 
Diccionario de Python
[Python Dictionary](/community/tutorials/python-dictionary) is an unordered sequence of data of key-value pair form. It is similar to the hash table type. Dictionaries are written within curly braces in the form `key:value`. It is very useful to retrieve data in an optimized way among a large amount of data.
```
#a sample dictionary variable
a = {1:"first name",2:"last name", "age":33}
#print value having key=1
print(a[1])
#print value having key=2
print(a[2])
#print value having key="age"
print(a["age"])
```
If you run this python dictionary data type example code, the output will be like the below image. Eso es todo por hoy sobre los tipos de datos de Python. No olvides ejecutar cada fragmento de código en tu máquina. Además, no te limites a copiar y pegar. Intenta escribir las líneas de código tú mismo.
